The article systematises the methodological foundations for applying predictive analytics and artificial-intelligence (AI) models to proactive reputation-risk management. Its purpose is to analyse the distinctive features of crisis public-relations practice that integrates predictive analytics and AI-based models. The methodological basis rests on a systematic review of key scholarly publications from recent years devoted to machine learning, natural language processing (NLP), and their adoption in public-relations practice. A multilayer architecture for proactive integrated crisis management (PICM-AI) is proposed, encompassing data collection and monitoring, predictive analysis, scenario simulation, and automated preparation of communication materials. Particular attention is paid to the model’s practical advantages—reduced response time to emerging threats and increased objectivity in decision-making—as well as to major challenges, including ethical dilemmas, data-bias risk, and the demand for highly qualified personnel. The findings are expected to interest communication scholars, PR practitioners, and executives seeking to enhance organisational resilience to reputational crises.

crisis communication, public relations, artificial intelligence, predictive analytics, machine learning, natural language processing, reputation management, proactive management, NLP, crisis PR

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